{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remote data access using pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pandas library enables access to data displayed on websites using the `read_html()` function and access to the API endpoints of various data providers through the related `pandas-datareader` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:06.402841Z",
     "start_time": "2020-06-16T16:31:06.398254Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:07.643241Z",
     "start_time": "2020-06-16T16:31:06.405846Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "import matplotlib.pyplot as plt\n",
    "import mplfinance as mpf\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download html table with SP500 constituents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The download of the content of one or more html tables works as follows, for instance for the constituents of the S&P500 index from Wikipedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:08.846591Z",
     "start_time": "2020-06-16T16:31:07.644941Z"
    }
   },
   "outputs": [],
   "source": [
    "sp_url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'\n",
    "sp500_constituents = pd.read_html(sp_url, header=0)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:08.964361Z",
     "start_time": "2020-06-16T16:31:08.854647Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 505 entries, 0 to 504\n",
      "Data columns (total 9 columns):\n",
      " #   Column                 Non-Null Count  Dtype \n",
      "---  ------                 --------------  ----- \n",
      " 0   Symbol                 505 non-null    object\n",
      " 1   Security               505 non-null    object\n",
      " 2   SEC filings            505 non-null    object\n",
      " 3   GICS Sector            505 non-null    object\n",
      " 4   GICS Sub Industry      505 non-null    object\n",
      " 5   Headquarters Location  505 non-null    object\n",
      " 6   Date first added       408 non-null    object\n",
      " 7   CIK                    505 non-null    int64 \n",
      " 8   Founded                234 non-null    object\n",
      "dtypes: int64(1), object(8)\n",
      "memory usage: 35.6+ KB\n"
     ]
    }
   ],
   "source": [
    "sp500_constituents.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:08.994696Z",
     "start_time": "2020-06-16T16:31:08.966011Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Security</th>\n",
       "      <th>SEC filings</th>\n",
       "      <th>GICS Sector</th>\n",
       "      <th>GICS Sub Industry</th>\n",
       "      <th>Headquarters Location</th>\n",
       "      <th>Date first added</th>\n",
       "      <th>CIK</th>\n",
       "      <th>Founded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMM</td>\n",
       "      <td>3M Company</td>\n",
       "      <td>reports</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Conglomerates</td>\n",
       "      <td>St. Paul, Minnesota</td>\n",
       "      <td>1976-08-09</td>\n",
       "      <td>66740</td>\n",
       "      <td>1902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABT</td>\n",
       "      <td>Abbott Laboratories</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>1964-03-31</td>\n",
       "      <td>1800</td>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABBV</td>\n",
       "      <td>AbbVie Inc.</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Pharmaceuticals</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1551152</td>\n",
       "      <td>2013 (1888)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABMD</td>\n",
       "      <td>ABIOMED Inc</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>Danvers, Massachusetts</td>\n",
       "      <td>2018-05-31</td>\n",
       "      <td>815094</td>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACN</td>\n",
       "      <td>Accenture plc</td>\n",
       "      <td>reports</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>IT Consulting &amp; Other Services</td>\n",
       "      <td>Dublin, Ireland</td>\n",
       "      <td>2011-07-06</td>\n",
       "      <td>1467373</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Symbol             Security SEC filings             GICS Sector  \\\n",
       "0    MMM           3M Company     reports             Industrials   \n",
       "1    ABT  Abbott Laboratories     reports             Health Care   \n",
       "2   ABBV          AbbVie Inc.     reports             Health Care   \n",
       "3   ABMD          ABIOMED Inc     reports             Health Care   \n",
       "4    ACN        Accenture plc     reports  Information Technology   \n",
       "\n",
       "                GICS Sub Industry    Headquarters Location Date first added  \\\n",
       "0        Industrial Conglomerates      St. Paul, Minnesota       1976-08-09   \n",
       "1           Health Care Equipment  North Chicago, Illinois       1964-03-31   \n",
       "2                 Pharmaceuticals  North Chicago, Illinois       2012-12-31   \n",
       "3           Health Care Equipment   Danvers, Massachusetts       2018-05-31   \n",
       "4  IT Consulting & Other Services          Dublin, Ireland       2011-07-06   \n",
       "\n",
       "       CIK      Founded  \n",
       "0    66740         1902  \n",
       "1     1800         1888  \n",
       "2  1551152  2013 (1888)  \n",
       "3   815094         1981  \n",
       "4  1467373         1989  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500_constituents.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas-datareader for Market Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pandas` used to facilitate access to data providers' APIs directly, but this functionality has moved to the related pandas-datareader library. The stability of the APIs varies with provider policies, and as of June 2o18 at version 0.7, the following sources are available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See [documentation](https://pandas-datareader.readthedocs.io/en/latest/); functionality frequently changes as underlying provider APIs evolve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yahoo Finance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:09.619351Z",
     "start_time": "2020-06-16T16:31:08.997931Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 855 entries, 2014-01-02 to 2017-05-24\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   High       855 non-null    float64\n",
      " 1   Low        855 non-null    float64\n",
      " 2   Open       855 non-null    float64\n",
      " 3   Close      855 non-null    float64\n",
      " 4   Volume     855 non-null    int64  \n",
      " 5   Adj Close  855 non-null    float64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 46.8 KB\n"
     ]
    }
   ],
   "source": [
    "start = '2014'\n",
    "end = datetime(2017, 5, 24)\n",
    "\n",
    "yahoo= web.DataReader('FB', 'yahoo', start=start, end=end)\n",
    "yahoo.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:10.159122Z",
     "start_time": "2020-06-16T16:31:09.621796Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpf.plot(yahoo.drop('Adj Close', axis=1), type='candle')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IEX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IEX is an alternative exchange started in response to the HFT controversy and portrayed in Michael Lewis' controversial Flash Boys. It aims to slow down the speed of trading to create a more level playing field and has been growing rapidly since launch in 2016 while still small with a market share of around 2.5% in June 2018."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note:** IEX now requires an [API](https://iexcloud.io/) key after registration for (free) account that you can store as environment variable and retrieve as illustrated below, or pass directly via keyword argument to `pandas_datareader`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:10.165605Z",
     "start_time": "2020-06-16T16:31:10.161455Z"
    }
   },
   "outputs": [],
   "source": [
    "IEX_API_KEY=os.getenv('IEX_API_KEY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:10.536348Z",
     "start_time": "2020-06-16T16:31:10.167938Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1260 entries, 2015-06-16 to 2020-06-15\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   open    1260 non-null   float64\n",
      " 1   high    1260 non-null   float64\n",
      " 2   low     1260 non-null   float64\n",
      " 3   close   1260 non-null   float64\n",
      " 4   volume  1260 non-null   int64  \n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 59.1+ KB\n"
     ]
    }
   ],
   "source": [
    "start = datetime(2015, 2, 9)\n",
    "# end = datetime(2017, 5, 24)\n",
    "\n",
    "iex = web.DataReader('FB', 'iex', start, api_key=IEX_API_KEY)\n",
    "iex.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:10.562347Z",
     "start_time": "2020-06-16T16:31:10.538917Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-06-10</th>\n",
       "      <td>240.96</td>\n",
       "      <td>241.21</td>\n",
       "      <td>235.28</td>\n",
       "      <td>236.73</td>\n",
       "      <td>20720709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-11</th>\n",
       "      <td>229.94</td>\n",
       "      <td>232.89</td>\n",
       "      <td>223.55</td>\n",
       "      <td>224.43</td>\n",
       "      <td>26708163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-12</th>\n",
       "      <td>229.90</td>\n",
       "      <td>231.66</td>\n",
       "      <td>224.50</td>\n",
       "      <td>228.58</td>\n",
       "      <td>22091387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-12</th>\n",
       "      <td>229.90</td>\n",
       "      <td>231.66</td>\n",
       "      <td>224.50</td>\n",
       "      <td>228.58</td>\n",
       "      <td>22091387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-15</th>\n",
       "      <td>225.09</td>\n",
       "      <td>233.77</td>\n",
       "      <td>224.80</td>\n",
       "      <td>232.50</td>\n",
       "      <td>15340264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              open    high     low   close    volume\n",
       "date                                                \n",
       "2020-06-10  240.96  241.21  235.28  236.73  20720709\n",
       "2020-06-11  229.94  232.89  223.55  224.43  26708163\n",
       "2020-06-12  229.90  231.66  224.50  228.58  22091387\n",
       "2020-06-12  229.90  231.66  224.50  228.58  22091387\n",
       "2020-06-15  225.09  233.77  224.80  232.50  15340264"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iex.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:10.873114Z",
     "start_time": "2020-06-16T16:31:10.568669Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "iex.close.plot(figsize=(14, 5))\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Book Data\n",
    "\n",
    "In addition to historical EOD price and volume data, IEX provides real-time depth of book quotations that offer an aggregated size of orders by price and side. This service also includes last trade price and size information.\n",
    "\n",
    "DEEP is used to receive real-time depth of book quotations direct from IEX. The depth of book quotations received via DEEP provide an aggregated size of resting displayed orders at a price and side, and do not indicate the size or number of individual orders at any price level. Non-displayed orders and non-displayed portions of reserve orders are not represented in DEEP.\n",
    "\n",
    "DEEP also provides last trade price and size information. Trades resulting from either displayed or non-displayed orders matching on IEX will be reported. Routed executions will not be reported."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only works on trading days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:11.041674Z",
     "start_time": "2020-06-16T16:31:10.878170Z"
    }
   },
   "outputs": [],
   "source": [
    "book = web.get_iex_book('AAPL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:11.053523Z",
     "start_time": "2020-06-16T16:31:11.044219Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['symbol',\n",
       " 'marketPercent',\n",
       " 'volume',\n",
       " 'lastSalePrice',\n",
       " 'lastSaleSize',\n",
       " 'lastSaleTime',\n",
       " 'lastUpdated',\n",
       " 'bids',\n",
       " 'asks',\n",
       " 'systemEvent',\n",
       " 'tradingStatus',\n",
       " 'opHaltStatus',\n",
       " 'ssrStatus',\n",
       " 'securityEvent',\n",
       " 'trades',\n",
       " 'tradeBreaks']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(book.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:11.081754Z",
     "start_time": "2020-06-16T16:31:11.056964Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>size</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>side</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>345.97</td>\n",
       "      <td>100</td>\n",
       "      <td>1592324338120</td>\n",
       "      <td>bids</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>333.00</td>\n",
       "      <td>100</td>\n",
       "      <td>1592314202312</td>\n",
       "      <td>bids</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>265.00</td>\n",
       "      <td>100</td>\n",
       "      <td>1592314201735</td>\n",
       "      <td>bids</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>250.00</td>\n",
       "      <td>100</td>\n",
       "      <td>1592314201757</td>\n",
       "      <td>bids</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230.00</td>\n",
       "      <td>500</td>\n",
       "      <td>1592314201706</td>\n",
       "      <td>bids</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    price  size      timestamp  side\n",
       "0  345.97   100  1592324338120  bids\n",
       "1  333.00   100  1592314202312  bids\n",
       "2  265.00   100  1592314201735  bids\n",
       "3  250.00   100  1592314201757  bids\n",
       "4  230.00   500  1592314201706  bids"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders = pd.concat([pd.DataFrame(book[side]).assign(side=side) for side in ['bids', 'asks']])\n",
    "orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:11.134468Z",
     "start_time": "2020-06-16T16:31:11.087324Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "symbol\n",
      "AAPL\n",
      "\n",
      "marketPercent\n",
      "0.00724\n",
      "\n",
      "volume\n",
      "159394\n",
      "\n",
      "lastSalePrice\n",
      "351.13\n",
      "\n",
      "lastSaleSize\n",
      "100\n",
      "\n",
      "lastSaleTime\n",
      "1592325061234\n",
      "\n",
      "lastUpdated\n",
      "1592325058468\n",
      "\n",
      "bids\n",
      "    price  size      timestamp\n",
      "0  345.97   100  1592324338120\n",
      "1  333.00   100  1592314202312\n",
      "2  265.00   100  1592314201735\n",
      "3  250.00   100  1592314201757\n",
      "4  230.00   500  1592314201706\n",
      "5  229.18   650  1592314201767\n",
      "6  225.00   100  1592314202316\n",
      "\n",
      "asks\n",
      "    price  size      timestamp\n",
      "0  353.03   100  1592324338217\n",
      "1  356.00   100  1592314202372\n",
      "2  365.50   100  1592314201783\n",
      "\n",
      "systemEvent\n",
      "{'systemEvent': 'R', 'timestamp': 1592314200003}\n",
      "\n",
      "tradingStatus\n",
      "{'status': 'T', 'reason': '    ', 'timestamp': 1592306401092}\n",
      "\n",
      "opHaltStatus\n",
      "{'isHalted': False, 'timestamp': 1592306401092}\n",
      "\n",
      "ssrStatus\n",
      "{'isSSR': False, 'detail': ' ', 'timestamp': 1592306401092}\n",
      "\n",
      "securityEvent\n",
      "{'securityEvent': 'MarketOpen', 'timestamp': 1592314200003}\n",
      "\n",
      "trades\n",
      "      price  size     tradeId  isISO  isOddLot  isOutsideRegularHours  \\\n",
      "0   351.130   100  1623683187  False     False                  False   \n",
      "1   351.050    12  1623540347   True      True                  False   \n",
      "2   350.880   100  1622871839  False     False                  False   \n",
      "3   350.900   100  1622739016  False     False                  False   \n",
      "4   350.900   100  1622737443  False     False                  False   \n",
      "5   350.905   100  1622244962  False     False                  False   \n",
      "6   350.880   100  1622002691   True     False                  False   \n",
      "7   350.785    28  1621488107  False      True                  False   \n",
      "8   350.860   100  1620777583   True     False                  False   \n",
      "9   350.775   100  1620595800  False     False                  False   \n",
      "10  350.650   100  1620333384  False     False                  False   \n",
      "11  350.610   100  1619937107   True     False                  False   \n",
      "12  350.620   100  1619894773  False     False                  False   \n",
      "13  350.620   159  1618499276  False     False                  False   \n",
      "14  350.640    50  1618357241   True      True                  False   \n",
      "15  350.540     9  1617509491  False      True                  False   \n",
      "16  350.560   100  1616541743   True     False                  False   \n",
      "17  350.570   100  1616516854  False     False                  False   \n",
      "18  350.430   200  1615536751   True     False                  False   \n",
      "19  350.430   100  1615536741   True     False                  False   \n",
      "\n",
      "    isSinglePriceCross  isTradeThroughExempt      timestamp  \n",
      "0                False                 False  1592325061234  \n",
      "1                False                 False  1592325060778  \n",
      "2                False                 False  1592325057920  \n",
      "3                False                 False  1592325056419  \n",
      "4                False                 False  1592325056408  \n",
      "5                False                 False  1592325050721  \n",
      "6                False                 False  1592325048299  \n",
      "7                False                 False  1592325044843  \n",
      "8                False                  True  1592325042308  \n",
      "9                False                 False  1592325042210  \n",
      "10               False                 False  1592325041936  \n",
      "11               False                 False  1592325035898  \n",
      "12               False                 False  1592325035197  \n",
      "13               False                 False  1592325016970  \n",
      "14               False                 False  1592325015586  \n",
      "15               False                 False  1592325010057  \n",
      "16               False                 False  1592324999896  \n",
      "17               False                 False  1592324999166  \n",
      "18               False                 False  1592324981850  \n",
      "19               False                 False  1592324981850  \n",
      "\n",
      "tradeBreaks\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "for key in book.keys():\n",
    "    try:\n",
    "        print(f'\\n{key}')\n",
    "        print(pd.DataFrame(book[key]))\n",
    "    except:\n",
    "        print(book[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:11.172369Z",
     "start_time": "2020-06-16T16:31:11.139798Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>size</th>\n",
       "      <th>tradeId</th>\n",
       "      <th>isISO</th>\n",
       "      <th>isOddLot</th>\n",
       "      <th>isOutsideRegularHours</th>\n",
       "      <th>isSinglePriceCross</th>\n",
       "      <th>isTradeThroughExempt</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>351.13</td>\n",
       "      <td>100</td>\n",
       "      <td>1623683187</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1592325061234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>351.05</td>\n",
       "      <td>12</td>\n",
       "      <td>1623540347</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1592325060778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>350.88</td>\n",
       "      <td>100</td>\n",
       "      <td>1622871839</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1592325057920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>350.90</td>\n",
       "      <td>100</td>\n",
       "      <td>1622739016</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1592325056419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>350.90</td>\n",
       "      <td>100</td>\n",
       "      <td>1622737443</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1592325056408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    price  size     tradeId  isISO  isOddLot  isOutsideRegularHours  \\\n",
       "0  351.13   100  1623683187  False     False                  False   \n",
       "1  351.05    12  1623540347   True      True                  False   \n",
       "2  350.88   100  1622871839  False     False                  False   \n",
       "3  350.90   100  1622739016  False     False                  False   \n",
       "4  350.90   100  1622737443  False     False                  False   \n",
       "\n",
       "   isSinglePriceCross  isTradeThroughExempt      timestamp  \n",
       "0               False                 False  1592325061234  \n",
       "1               False                 False  1592325060778  \n",
       "2               False                 False  1592325057920  \n",
       "3               False                 False  1592325056419  \n",
       "4               False                 False  1592325056408  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(book['trades']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quandl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtain Quandl [API Key](https://www.quandl.com/tools/api) and store in environment variable as `QUANDL_API_KEY`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:11.863946Z",
     "start_time": "2020-06-16T16:31:11.180937Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 813 entries, 2018-03-27 to 2015-01-02\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Open        813 non-null    float64\n",
      " 1   High        813 non-null    float64\n",
      " 2   Low         813 non-null    float64\n",
      " 3   Close       813 non-null    float64\n",
      " 4   Volume      813 non-null    float64\n",
      " 5   ExDividend  813 non-null    float64\n",
      " 6   SplitRatio  813 non-null    float64\n",
      " 7   AdjOpen     813 non-null    float64\n",
      " 8   AdjHigh     813 non-null    float64\n",
      " 9   AdjLow      813 non-null    float64\n",
      " 10  AdjClose    813 non-null    float64\n",
      " 11  AdjVolume   813 non-null    float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 82.6 KB\n"
     ]
    }
   ],
   "source": [
    "symbol = 'FB.US'\n",
    "\n",
    "quandl = web.DataReader(symbol, 'quandl', '2015-01-01')\n",
    "quandl.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FRED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:12.385174Z",
     "start_time": "2020-06-16T16:31:11.865572Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 13 entries, 2010-01-01 to 2013-01-01\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   GDP     13 non-null     float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 208.0 bytes\n"
     ]
    }
   ],
   "source": [
    "start = datetime(2010, 1, 1)\n",
    "\n",
    "end = datetime(2013, 1, 27)\n",
    "\n",
    "gdp = web.DataReader('GDP', 'fred', start, end)\n",
    "\n",
    "gdp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:13.081034Z",
     "start_time": "2020-06-16T16:31:12.386552Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 37 entries, 2010-01-01 to 2013-01-01\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   CPIAUCSL  37 non-null     float64\n",
      " 1   CPILFESL  37 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 888.0 bytes\n"
     ]
    }
   ],
   "source": [
    "inflation = web.DataReader(['CPIAUCSL', 'CPILFESL'], 'fred', start, end)\n",
    "inflation.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fama/French"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:13.295565Z",
     "start_time": "2020-06-16T16:31:13.082676Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F-F_Research_Data_Factors',\n",
       " 'F-F_Research_Data_Factors_weekly',\n",
       " 'F-F_Research_Data_Factors_daily',\n",
       " 'F-F_Research_Data_5_Factors_2x3',\n",
       " 'F-F_Research_Data_5_Factors_2x3_daily',\n",
       " 'Portfolios_Formed_on_ME',\n",
       " 'Portfolios_Formed_on_ME_Wout_Div',\n",
       " 'Portfolios_Formed_on_ME_Daily',\n",
       " 'Portfolios_Formed_on_BE-ME',\n",
       " 'Portfolios_Formed_on_BE-ME_Wout_Div',\n",
       " 'Portfolios_Formed_on_BE-ME_Daily',\n",
       " 'Portfolios_Formed_on_OP',\n",
       " 'Portfolios_Formed_on_OP_Wout_Div',\n",
       " 'Portfolios_Formed_on_OP_Daily',\n",
       " 'Portfolios_Formed_on_INV',\n",
       " 'Portfolios_Formed_on_INV_Wout_Div',\n",
       " 'Portfolios_Formed_on_INV_Daily',\n",
       " '6_Portfolios_2x3',\n",
       " '6_Portfolios_2x3_Wout_Div',\n",
       " '6_Portfolios_2x3_weekly',\n",
       " '6_Portfolios_2x3_daily',\n",
       " '25_Portfolios_5x5',\n",
       " '25_Portfolios_5x5_Wout_Div',\n",
       " '25_Portfolios_5x5_Daily',\n",
       " '100_Portfolios_10x10',\n",
       " '100_Portfolios_10x10_Wout_Div',\n",
       " '100_Portfolios_10x10_Daily',\n",
       " '6_Portfolios_ME_OP_2x3',\n",
       " '6_Portfolios_ME_OP_2x3_Wout_Div',\n",
       " '6_Portfolios_ME_OP_2x3_daily',\n",
       " '25_Portfolios_ME_OP_5x5',\n",
       " '25_Portfolios_ME_OP_5x5_Wout_Div',\n",
       " '25_Portfolios_ME_OP_5x5_daily',\n",
       " '100_Portfolios_ME_OP_10x10',\n",
       " '100_Portfolios_10x10_ME_OP_Wout_Div',\n",
       " '100_Portfolios_ME_OP_10x10_daily',\n",
       " '6_Portfolios_ME_INV_2x3',\n",
       " '6_Portfolios_ME_INV_2x3_Wout_Div',\n",
       " '6_Portfolios_ME_INV_2x3_daily',\n",
       " '25_Portfolios_ME_INV_5x5',\n",
       " '25_Portfolios_ME_INV_5x5_Wout_Div',\n",
       " '25_Portfolios_ME_INV_5x5_daily',\n",
       " '100_Portfolios_ME_INV_10x10',\n",
       " '100_Portfolios_10x10_ME_INV_Wout_Div',\n",
       " '100_Portfolios_ME_INV_10x10_daily',\n",
       " '25_Portfolios_BEME_OP_5x5',\n",
       " '25_Portfolios_BEME_OP_5x5_Wout_Div',\n",
       " '25_Portfolios_BEME_OP_5x5_daily',\n",
       " '25_Portfolios_BEME_INV_5x5',\n",
       " '25_Portfolios_BEME_INV_5x5_Wout_Div',\n",
       " '25_Portfolios_BEME_INV_5x5_daily',\n",
       " '25_Portfolios_OP_INV_5x5',\n",
       " '25_Portfolios_OP_INV_5x5_Wout_Div',\n",
       " '25_Portfolios_OP_INV_5x5_daily',\n",
       " '32_Portfolios_ME_BEME_OP_2x4x4',\n",
       " '32_Portfolios_ME_BEME_OP_2x4x4_Wout_Div',\n",
       " '32_Portfolios_ME_BEME_INV_2x4x4',\n",
       " '32_Portfolios_ME_BEME_INV_2x4x4_Wout_Div',\n",
       " '32_Portfolios_ME_OP_INV_2x4x4',\n",
       " '32_Portfolios_ME_OP_INV_2x4x4_Wout_Div',\n",
       " 'Portfolios_Formed_on_E-P',\n",
       " 'Portfolios_Formed_on_E-P_Wout_Div',\n",
       " 'Portfolios_Formed_on_CF-P',\n",
       " 'Portfolios_Formed_on_CF-P_Wout_Div',\n",
       " 'Portfolios_Formed_on_D-P',\n",
       " 'Portfolios_Formed_on_D-P_Wout_Div',\n",
       " '6_Portfolios_ME_EP_2x3',\n",
       " '6_Portfolios_ME_EP_2x3_Wout_Div',\n",
       " '6_Portfolios_ME_CFP_2x3',\n",
       " '6_Portfolios_ME_CFP_2x3_Wout_Div',\n",
       " '6_Portfolios_ME_DP_2x3',\n",
       " '6_Portfolios_ME_DP_2x3_Wout_Div',\n",
       " 'F-F_Momentum_Factor',\n",
       " 'F-F_Momentum_Factor_daily',\n",
       " '6_Portfolios_ME_Prior_12_2',\n",
       " '6_Portfolios_ME_Prior_12_2_Daily',\n",
       " '25_Portfolios_ME_Prior_12_2',\n",
       " '25_Portfolios_ME_Prior_12_2_Daily',\n",
       " '10_Portfolios_Prior_12_2',\n",
       " '10_Portfolios_Prior_12_2_Daily',\n",
       " 'F-F_ST_Reversal_Factor',\n",
       " 'F-F_ST_Reversal_Factor_daily',\n",
       " '6_Portfolios_ME_Prior_1_0',\n",
       " '6_Portfolios_ME_Prior_1_0_Daily',\n",
       " '25_Portfolios_ME_Prior_1_0',\n",
       " '25_Portfolios_ME_Prior_1_0_Daily',\n",
       " '10_Portfolios_Prior_1_0',\n",
       " '10_Portfolios_Prior_1_0_Daily',\n",
       " 'F-F_LT_Reversal_Factor',\n",
       " 'F-F_LT_Reversal_Factor_daily',\n",
       " '6_Portfolios_ME_Prior_60_13',\n",
       " '6_Portfolios_ME_Prior_60_13_Daily',\n",
       " '25_Portfolios_ME_Prior_60_13',\n",
       " '25_Portfolios_ME_Prior_60_13_Daily',\n",
       " '10_Portfolios_Prior_60_13',\n",
       " '10_Portfolios_Prior_60_13_Daily',\n",
       " 'Portfolios_Formed_on_AC',\n",
       " '25_Portfolios_ME_AC_5x5',\n",
       " 'Portfolios_Formed_on_BETA',\n",
       " '25_Portfolios_ME_BETA_5x5',\n",
       " 'Portfolios_Formed_on_NI',\n",
       " '25_Portfolios_ME_NI_5x5',\n",
       " 'Portfolios_Formed_on_VAR',\n",
       " '25_Portfolios_ME_VAR_5x5',\n",
       " 'Portfolios_Formed_on_RESVAR',\n",
       " '25_Portfolios_ME_RESVAR_5x5',\n",
       " '5_Industry_Portfolios',\n",
       " '5_Industry_Portfolios_Wout_Div',\n",
       " '5_Industry_Portfolios_daily',\n",
       " '10_Industry_Portfolios',\n",
       " '10_Industry_Portfolios_Wout_Div',\n",
       " '10_Industry_Portfolios_daily',\n",
       " '12_Industry_Portfolios',\n",
       " '12_Industry_Portfolios_Wout_Div',\n",
       " '12_Industry_Portfolios_daily',\n",
       " '17_Industry_Portfolios',\n",
       " '17_Industry_Portfolios_Wout_Div',\n",
       " '17_Industry_Portfolios_daily',\n",
       " '30_Industry_Portfolios',\n",
       " '30_Industry_Portfolios_Wout_Div',\n",
       " '30_Industry_Portfolios_daily',\n",
       " '38_Industry_Portfolios',\n",
       " '38_Industry_Portfolios_Wout_Div',\n",
       " '38_Industry_Portfolios_daily',\n",
       " '48_Industry_Portfolios',\n",
       " '48_Industry_Portfolios_Wout_Div',\n",
       " '48_Industry_Portfolios_daily',\n",
       " '49_Industry_Portfolios',\n",
       " '49_Industry_Portfolios_Wout_Div',\n",
       " '49_Industry_Portfolios_daily',\n",
       " 'ME_Breakpoints',\n",
       " 'BE-ME_Breakpoints',\n",
       " 'OP_Breakpoints',\n",
       " 'INV_Breakpoints',\n",
       " 'E-P_Breakpoints',\n",
       " 'CF-P_Breakpoints',\n",
       " 'D-P_Breakpoints',\n",
       " 'Prior_2-12_Breakpoints',\n",
       " 'Developed_3_Factors',\n",
       " 'Developed_3_Factors_Daily',\n",
       " 'Developed_ex_US_3_Factors',\n",
       " 'Developed_ex_US_3_Factors_Daily',\n",
       " 'Europe_3_Factors',\n",
       " 'Europe_3_Factors_Daily',\n",
       " 'Japan_3_Factors',\n",
       " 'Japan_3_Factors_Daily',\n",
       " 'Asia_Pacific_ex_Japan_3_Factors',\n",
       " 'Asia_Pacific_ex_Japan_3_Factors_Daily',\n",
       " 'North_America_3_Factors',\n",
       " 'North_America_3_Factors_Daily',\n",
       " 'Developed_5_Factors',\n",
       " 'Developed_5_Factors_Daily',\n",
       " 'Developed_ex_US_5_Factors',\n",
       " 'Developed_ex_US_5_Factors_Daily',\n",
       " 'Europe_5_Factors',\n",
       " 'Europe_5_Factors_Daily',\n",
       " 'Japan_5_Factors',\n",
       " 'Japan_5_Factors_Daily',\n",
       " 'Asia_Pacific_ex_Japan_5_Factors',\n",
       " 'Asia_Pacific_ex_Japan_5_Factors_Daily',\n",
       " 'North_America_5_Factors',\n",
       " 'North_America_5_Factors_Daily',\n",
       " 'Developed_Mom_Factor',\n",
       " 'Developed_Mom_Factor_Daily',\n",
       " 'Developed_ex_US_Mom_Factor',\n",
       " 'Developed_ex_US_Mom_Factor_Daily',\n",
       " 'Europe_Mom_Factor',\n",
       " 'Europe_Mom_Factor_Daily',\n",
       " 'Japan_Mom_Factor',\n",
       " 'Japan_Mom_Factor_Daily',\n",
       " 'Asia_Pacific_ex_Japan_MOM_Factor',\n",
       " 'Asia_Pacific_ex_Japan_MOM_Factor_Daily',\n",
       " 'North_America_Mom_Factor',\n",
       " 'North_America_Mom_Factor_Daily',\n",
       " 'Developed_6_Portfolios_ME_BE-ME',\n",
       " 'Developed_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Developed_ex_US_6_Portfolios_ME_BE-ME',\n",
       " 'Developed_ex_US_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Europe_6_Portfolios_ME_BE-ME',\n",
       " 'Europe_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Japan_6_Portfolios_ME_BE-ME',\n",
       " 'Japan_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_BE-ME',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_BE-ME_daily',\n",
       " 'North_America_6_Portfolios_ME_BE-ME',\n",
       " 'North_America_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Developed_25_Portfolios_ME_BE-ME',\n",
       " 'Developed_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Developed_ex_US_25_Portfolios_ME_BE-ME',\n",
       " 'Developed_ex_US_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Europe_25_Portfolios_ME_BE-ME',\n",
       " 'Europe_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Japan_25_Portfolios_ME_BE-ME',\n",
       " 'Japan_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_BE-ME',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_BE-ME_daily',\n",
       " 'North_America_25_Portfolios_ME_BE-ME',\n",
       " 'North_America_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Developed_6_Portfolios_ME_OP',\n",
       " 'Developed_6_Portfolios_ME_OP_Daily',\n",
       " 'Developed_ex_US_6_Portfolios_ME_OP',\n",
       " 'Developed_ex_US_6_Portfolios_ME_OP_Daily',\n",
       " 'Europe_6_Portfolios_ME_OP',\n",
       " 'Europe_6_Portfolios_ME_OP_Daily',\n",
       " 'Japan_6_Portfolios_ME_OP',\n",
       " 'Japan_6_Portfolios_ME_OP_Daily',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_OP',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_OP_Daily',\n",
       " 'North_America_6_Portfolios_ME_OP',\n",
       " 'North_America_6_Portfolios_ME_OP_Daily',\n",
       " 'Developed_25_Portfolios_ME_OP',\n",
       " 'Developed_25_Portfolios_ME_OP_Daily',\n",
       " 'Developed_ex_US_25_Portfolios_ME_OP',\n",
       " 'Developed_ex_US_25_Portfolios_ME_OP_Daily',\n",
       " 'Europe_25_Portfolios_ME_OP',\n",
       " 'Europe_25_Portfolios_ME_OP_Daily',\n",
       " 'Japan_25_Portfolios_ME_OP',\n",
       " 'Japan_25_Portfolios_ME_OP_Daily',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_OP',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_OP_Daily',\n",
       " 'North_America_25_Portfolios_ME_OP',\n",
       " 'North_America_25_Portfolios_ME_OP_Daily',\n",
       " 'Developed_6_Portfolios_ME_INV',\n",
       " 'Developed_6_Portfolios_ME_INV_Daily',\n",
       " 'Developed_ex_US_6_Portfolios_ME_INV',\n",
       " 'Developed_ex_US_6_Portfolios_ME_INV_Daily',\n",
       " 'Europe_6_Portfolios_ME_INV',\n",
       " 'Europe_6_Portfolios_ME_INV_Daily',\n",
       " 'Japan_6_Portfolios_ME_INV',\n",
       " 'Japan_6_Portfolios_ME_INV_Daily',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_INV',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_INV_Daily',\n",
       " 'North_America_6_Portfolios_ME_INV',\n",
       " 'North_America_6_Portfolios_ME_INV_Daily',\n",
       " 'Developed_25_Portfolios_ME_INV',\n",
       " 'Developed_25_Portfolios_ME_INV_Daily',\n",
       " 'Developed_ex_US_25_Portfolios_ME_INV',\n",
       " 'Developed_ex_US_25_Portfolios_ME_INV_Daily',\n",
       " 'Europe_25_Portfolios_ME_INV',\n",
       " 'Europe_25_Portfolios_ME_INV_Daily',\n",
       " 'Japan_25_Portfolios_ME_INV',\n",
       " 'Japan_25_Portfolios_ME_INV_Daily',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_INV',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_INV_Daily',\n",
       " 'North_America_25_Portfolios_ME_INV',\n",
       " 'North_America_25_Portfolios_ME_INV_Daily',\n",
       " 'Developed_6_Portfolios_ME_Prior_12_2',\n",
       " 'Developed_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Developed_ex_US_6_Portfolios_ME_Prior_12_2',\n",
       " 'Developed_ex_US_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Europe_6_Portfolios_ME_Prior_12_2',\n",
       " 'Europe_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Japan_6_Portfolios_ME_Prior_12_2',\n",
       " 'Japan_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_Prior_12_2',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'North_America_6_Portfolios_ME_Prior_12_2',\n",
       " 'North_America_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Developed_25_Portfolios_ME_Prior_12_2',\n",
       " 'Developed_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Developed_ex_US_25_Portfolios_ME_Prior_12_2',\n",
       " 'Developed_ex_US_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Europe_25_Portfolios_ME_Prior_12_2',\n",
       " 'Europe_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Japan_25_Portfolios_ME_Prior_12_2',\n",
       " 'Japan_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_Prior_12_2',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'North_America_25_Portfolios_ME_Prior_12_2',\n",
       " 'North_America_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Developed_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Developed_ex_US_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Europe_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Japan_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Asia_Pacific_ex_Japan_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'North_America_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Developed_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Developed_ex_US_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Europe_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Japan_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Asia_Pacific_ex_Japan_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'North_America_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Developed_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Developed_ex_US_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Europe_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Japan_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Asia_Pacific_ex_Japan_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'North_America_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Emerging_5_Factors',\n",
       " 'Emerging_MOM_Factor',\n",
       " 'Emerging_Markets_6_Portfolios_ME_BE-ME',\n",
       " 'Emerging_Markets_6_Portfolios_ME_OP',\n",
       " 'Emerging_Markets_6_Portfolios_ME_INV',\n",
       " 'Emerging_Markets_6_Portfolios_ME_Prior_12_2',\n",
       " 'Emerging_Markets_4_Portfolios_BE-ME_OP',\n",
       " 'Emerging_Markets_4_Portfolios_OP_INV',\n",
       " 'Emerging_Markets_4_Portfolios_BE-ME_INV']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas_datareader.famafrench import get_available_datasets\n",
    "get_available_datasets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:13.685900Z",
     "start_time": "2020-06-16T16:31:13.297565Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 Industry Portfolios\n",
      "---------------------\n",
      "\n",
      "This file was created by CMPT_IND_RETS using the 202004 CRSP database. It contains value- and equal-weighted returns for 5 industry portfolios. The portfolios are constructed at the end of June. The annual returns are from January to December. Missing data are indicated by -99.99 or -999. Copyright 2020 Kenneth R. French\n",
      "\n",
      "  0 : Average Value Weighted Returns -- Monthly (59 rows x 5 cols)\n",
      "  1 : Average Equal Weighted Returns -- Monthly (59 rows x 5 cols)\n",
      "  2 : Average Value Weighted Returns -- Annual (5 rows x 5 cols)\n",
      "  3 : Average Equal Weighted Returns -- Annual (5 rows x 5 cols)\n",
      "  4 : Number of Firms in Portfolios (59 rows x 5 cols)\n",
      "  5 : Average Firm Size (59 rows x 5 cols)\n",
      "  6 : Sum of BE / Sum of ME (5 rows x 5 cols)\n",
      "  7 : Value-Weighted Average of BE/ME (5 rows x 5 cols)\n"
     ]
    }
   ],
   "source": [
    "ds = web.DataReader('5_Industry_Portfolios', 'famafrench')\n",
    "print(ds['DESCR'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### World Bank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:20.351157Z",
     "start_time": "2020-06-16T16:31:13.687743Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>unit</th>\n",
       "      <th>source</th>\n",
       "      <th>sourceNote</th>\n",
       "      <th>sourceOrganization</th>\n",
       "      <th>topics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>6.0.GDPpc_constant</td>\n",
       "      <td>GDP per capita, PPP (constant 2011 internation...</td>\n",
       "      <td></td>\n",
       "      <td>LAC Equity Lab</td>\n",
       "      <td>GDP per capita based on purchasing power parit...</td>\n",
       "      <td>b'World Development Indicators (World Bank)'</td>\n",
       "      <td>Economy &amp; Growth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9258</th>\n",
       "      <td>NY.GDP.PCAP.KD</td>\n",
       "      <td>GDP per capita (constant 2010 US$)</td>\n",
       "      <td></td>\n",
       "      <td>World Development Indicators</td>\n",
       "      <td>GDP per capita is gross domestic product divid...</td>\n",
       "      <td>b'World Bank national accounts data, and OECD ...</td>\n",
       "      <td>Economy &amp; Growth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9260</th>\n",
       "      <td>NY.GDP.PCAP.KN</td>\n",
       "      <td>GDP per capita (constant LCU)</td>\n",
       "      <td></td>\n",
       "      <td>World Development Indicators</td>\n",
       "      <td>GDP per capita is gross domestic product divid...</td>\n",
       "      <td>b'World Bank national accounts data, and OECD ...</td>\n",
       "      <td>Economy &amp; Growth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9262</th>\n",
       "      <td>NY.GDP.PCAP.PP.KD</td>\n",
       "      <td>GDP per capita, PPP (constant 2017 internation...</td>\n",
       "      <td></td>\n",
       "      <td>World Development Indicators</td>\n",
       "      <td>GDP per capita based on purchasing power parit...</td>\n",
       "      <td>b'International Comparison Program, World Bank...</td>\n",
       "      <td>Economy &amp; Growth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9263</th>\n",
       "      <td>NY.GDP.PCAP.PP.KD.87</td>\n",
       "      <td>GDP per capita, PPP (constant 1987 internation...</td>\n",
       "      <td></td>\n",
       "      <td>WDI Database Archives</td>\n",
       "      <td></td>\n",
       "      <td>b''</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        id                                               name  \\\n",
       "680     6.0.GDPpc_constant  GDP per capita, PPP (constant 2011 internation...   \n",
       "9258        NY.GDP.PCAP.KD                 GDP per capita (constant 2010 US$)   \n",
       "9260        NY.GDP.PCAP.KN                      GDP per capita (constant LCU)   \n",
       "9262     NY.GDP.PCAP.PP.KD  GDP per capita, PPP (constant 2017 internation...   \n",
       "9263  NY.GDP.PCAP.PP.KD.87  GDP per capita, PPP (constant 1987 internation...   \n",
       "\n",
       "     unit                        source  \\\n",
       "680                      LAC Equity Lab   \n",
       "9258       World Development Indicators   \n",
       "9260       World Development Indicators   \n",
       "9262       World Development Indicators   \n",
       "9263              WDI Database Archives   \n",
       "\n",
       "                                             sourceNote  \\\n",
       "680   GDP per capita based on purchasing power parit...   \n",
       "9258  GDP per capita is gross domestic product divid...   \n",
       "9260  GDP per capita is gross domestic product divid...   \n",
       "9262  GDP per capita based on purchasing power parit...   \n",
       "9263                                                      \n",
       "\n",
       "                                     sourceOrganization            topics  \n",
       "680        b'World Development Indicators (World Bank)'  Economy & Growth  \n",
       "9258  b'World Bank national accounts data, and OECD ...  Economy & Growth  \n",
       "9260  b'World Bank national accounts data, and OECD ...  Economy & Growth  \n",
       "9262  b'International Comparison Program, World Bank...  Economy & Growth  \n",
       "9263                                                b''                    "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas_datareader import wb\n",
    "gdp_variables = wb.search('gdp.*capita.*const')\n",
    "gdp_variables.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:20.495020Z",
     "start_time": "2020-06-16T16:31:20.352495Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>NY.GDP.PCAP.KD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Canada</th>\n",
       "      <th>2019</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>51393.221416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>51146.481245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>50263.834327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>50279.585839</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              NY.GDP.PCAP.KD\n",
       "country year                \n",
       "Canada  2019             NaN\n",
       "        2018    51393.221416\n",
       "        2017    51146.481245\n",
       "        2016    50263.834327\n",
       "        2015    50279.585839"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb_data = wb.download(indicator='NY.GDP.PCAP.KD', \n",
    "                      country=['US', 'CA', 'MX'], \n",
    "                      start=1990, \n",
    "                      end=2019)\n",
    "wb_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OECD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:21.557343Z",
     "start_time": "2020-06-16T16:31:20.499816Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th colspan=\"6\" halign=\"left\">Japan</th>\n",
       "      <th colspan=\"6\" halign=\"left\">United States</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Administrative data</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Survey data</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Administrative data</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Survey data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series</th>\n",
       "      <th>Employees</th>\n",
       "      <th>Union members</th>\n",
       "      <th>Trade union  density</th>\n",
       "      <th>Employees</th>\n",
       "      <th>Union members</th>\n",
       "      <th>Trade union  density</th>\n",
       "      <th>Employees</th>\n",
       "      <th>Union members</th>\n",
       "      <th>Trade union  density</th>\n",
       "      <th>Employees</th>\n",
       "      <th>Union members</th>\n",
       "      <th>Trade union  density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>37100.0</td>\n",
       "      <td>12509.0</td>\n",
       "      <td>33.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80520.0</td>\n",
       "      <td>19634.0</td>\n",
       "      <td>24.4</td>\n",
       "      <td>80519.0</td>\n",
       "      <td>17403.0</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>37460.0</td>\n",
       "      <td>12437.0</td>\n",
       "      <td>33.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83482.0</td>\n",
       "      <td>19695.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>83481.0</td>\n",
       "      <td>19335.0</td>\n",
       "      <td>23.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>38990.0</td>\n",
       "      <td>12309.0</td>\n",
       "      <td>31.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89675.0</td>\n",
       "      <td>20055.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>89673.0</td>\n",
       "      <td>20986.0</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>40120.0</td>\n",
       "      <td>12369.0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89950.0</td>\n",
       "      <td>19843.0</td>\n",
       "      <td>22.1</td>\n",
       "      <td>89950.0</td>\n",
       "      <td>20095.0</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>41020.0</td>\n",
       "      <td>12526.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>42090.0</td>\n",
       "      <td>12520.0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91079.0</td>\n",
       "      <td>17717.0</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>43010.0</td>\n",
       "      <td>12418.0</td>\n",
       "      <td>28.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97406.0</td>\n",
       "      <td>16996.0</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>43830.0</td>\n",
       "      <td>12343.0</td>\n",
       "      <td>28.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99846.0</td>\n",
       "      <td>16975.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>45650.0</td>\n",
       "      <td>12227.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104642.0</td>\n",
       "      <td>17002.0</td>\n",
       "      <td>16.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country                  Japan                                                 \\\n",
       "Source     Administrative data                                    Survey data   \n",
       "Series               Employees Union members Trade union  density   Employees   \n",
       "Year                                                                            \n",
       "2010-01-01             37100.0       12509.0                 33.7         NaN   \n",
       "2011-01-01             37460.0       12437.0                 33.2         NaN   \n",
       "2012-01-01             38990.0       12309.0                 31.6         NaN   \n",
       "2013-01-01             40120.0       12369.0                 30.8         NaN   \n",
       "2014-01-01             41020.0       12526.0                 30.5         NaN   \n",
       "2015-01-01             42090.0       12520.0                 29.7         NaN   \n",
       "2016-01-01             43010.0       12418.0                 28.9         NaN   \n",
       "2017-01-01             43830.0       12343.0                 28.2         NaN   \n",
       "2018-01-01             45650.0       12227.0                 26.8         NaN   \n",
       "\n",
       "Country                                             United States  \\\n",
       "Source                                        Administrative data   \n",
       "Series     Union members Trade union  density           Employees   \n",
       "Year                                                                \n",
       "2010-01-01           NaN                  NaN             80520.0   \n",
       "2011-01-01           NaN                  NaN             83482.0   \n",
       "2012-01-01           NaN                  NaN             89675.0   \n",
       "2013-01-01           NaN                  NaN             89950.0   \n",
       "2014-01-01           NaN                  NaN                 NaN   \n",
       "2015-01-01           NaN                  NaN                 NaN   \n",
       "2016-01-01           NaN                  NaN                 NaN   \n",
       "2017-01-01           NaN                  NaN                 NaN   \n",
       "2018-01-01           NaN                  NaN                 NaN   \n",
       "\n",
       "Country                                                                  \\\n",
       "Source                                        Survey data                 \n",
       "Series     Union members Trade union  density   Employees Union members   \n",
       "Year                                                                      \n",
       "2010-01-01       19634.0                 24.4     80519.0       17403.0   \n",
       "2011-01-01       19695.0                 23.6     83481.0       19335.0   \n",
       "2012-01-01       20055.0                 22.4     89673.0       20986.0   \n",
       "2013-01-01       19843.0                 22.1     89950.0       20095.0   \n",
       "2014-01-01           NaN                  NaN         NaN           NaN   \n",
       "2015-01-01           NaN                  NaN     91079.0       17717.0   \n",
       "2016-01-01           NaN                  NaN     97406.0       16996.0   \n",
       "2017-01-01           NaN                  NaN     99846.0       16975.0   \n",
       "2018-01-01           NaN                  NaN    104642.0       17002.0   \n",
       "\n",
       "Country                          \n",
       "Source                           \n",
       "Series     Trade union  density  \n",
       "Year                             \n",
       "2010-01-01                 21.6  \n",
       "2011-01-01                 23.2  \n",
       "2012-01-01                 23.4  \n",
       "2013-01-01                 22.3  \n",
       "2014-01-01                  NaN  \n",
       "2015-01-01                 19.5  \n",
       "2016-01-01                 17.4  \n",
       "2017-01-01                 17.0  \n",
       "2018-01-01                 16.2  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = web.DataReader('TUD', 'oecd', start='2010', end='2019')\n",
    "df[['Japan', 'United States']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EuroStat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:24.338547Z",
     "start_time": "2020-06-16T16:31:21.561928Z"
    }
   },
   "outputs": [],
   "source": [
    "df = web.DataReader('tran_sf_railac', 'eurostat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:24.437638Z",
     "start_time": "2020-06-16T16:31:24.348309Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th>ACCIDENT</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Collisions of trains, including collisions with obstacles within the clearance gauge</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNIT</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Number</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Number</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEO</th>\n",
       "      <th>Austria</th>\n",
       "      <th>Belgium</th>\n",
       "      <th>Bulgaria</th>\n",
       "      <th>Switzerland</th>\n",
       "      <th>Channel Tunnel</th>\n",
       "      <th>Czechia</th>\n",
       "      <th>Germany (until 1990 former territory of the FRG)</th>\n",
       "      <th>Denmark</th>\n",
       "      <th>Estonia</th>\n",
       "      <th>Greece</th>\n",
       "      <th>...</th>\n",
       "      <th>Netherlands</th>\n",
       "      <th>Norway</th>\n",
       "      <th>Poland</th>\n",
       "      <th>Portugal</th>\n",
       "      <th>Romania</th>\n",
       "      <th>Sweden</th>\n",
       "      <th>Slovenia</th>\n",
       "      <th>Slovakia</th>\n",
       "      <th>Turkey</th>\n",
       "      <th>United Kingdom</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FREQ</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>...</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "      <th>Annual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIME_PERIOD</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 272 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ACCIDENT    Collisions of trains, including collisions with obstacles within the clearance gauge  \\\n",
       "UNIT                                                                                      Number   \n",
       "GEO                                                                                      Austria   \n",
       "FREQ                                                                                      Annual   \n",
       "TIME_PERIOD                                                                                        \n",
       "2016-01-01                                                 7.0                                     \n",
       "2017-01-01                                                 7.0                                     \n",
       "2018-01-01                                                 4.0                                     \n",
       "\n",
       "ACCIDENT                                                         \\\n",
       "UNIT                                                              \n",
       "GEO         Belgium Bulgaria Switzerland Channel Tunnel Czechia   \n",
       "FREQ         Annual   Annual      Annual         Annual  Annual   \n",
       "TIME_PERIOD                                                       \n",
       "2016-01-01      2.0      3.0         2.0            0.0     6.0   \n",
       "2017-01-01      1.0      1.0         3.0            0.0    11.0   \n",
       "2018-01-01      0.0      1.0         1.0            0.0     6.0   \n",
       "\n",
       "ACCIDENT                                                                      \\\n",
       "UNIT                                                                           \n",
       "GEO         Germany (until 1990 former territory of the FRG) Denmark Estonia   \n",
       "FREQ                                                  Annual  Annual  Annual   \n",
       "TIME_PERIOD                                                                    \n",
       "2016-01-01                                              29.0     3.0     3.0   \n",
       "2017-01-01                                              38.0     2.0     4.0   \n",
       "2018-01-01                                              40.0     1.0     0.0   \n",
       "\n",
       "ACCIDENT            ...     Unknown                                        \\\n",
       "UNIT                ...      Number                                         \n",
       "GEO         Greece  ... Netherlands Norway Poland Portugal Romania Sweden   \n",
       "FREQ        Annual  ...      Annual Annual Annual   Annual  Annual Annual   \n",
       "TIME_PERIOD         ...                                                     \n",
       "2016-01-01     1.0  ...         NaN    NaN    NaN      NaN     NaN    NaN   \n",
       "2017-01-01     1.0  ...         NaN    NaN    NaN      NaN     NaN    NaN   \n",
       "2018-01-01     2.0  ...         NaN    NaN    NaN      NaN     NaN    NaN   \n",
       "\n",
       "ACCIDENT                                             \n",
       "UNIT                                                 \n",
       "GEO         Slovenia Slovakia Turkey United Kingdom  \n",
       "FREQ          Annual   Annual Annual         Annual  \n",
       "TIME_PERIOD                                          \n",
       "2016-01-01       NaN      NaN    0.0            NaN  \n",
       "2017-01-01       NaN      NaN    0.0            NaN  \n",
       "2018-01-01       NaN      NaN    0.0            NaN  \n",
       "\n",
       "[3 rows x 272 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Stooq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Google finance stopped providing common index data download. The Stooq site had this data for download for a while but is currently broken, awaiting release of [fix](https://github.com/pydata/pandas-datareader/issues/594)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:25.992153Z",
     "start_time": "2020-06-16T16:31:24.443075Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_url = 'https://stooq.com/t/'\n",
    "ix = pd.read_html(index_url)\n",
    "len(ix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:26.758508Z",
     "start_time": "2020-06-16T16:31:25.997057Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1257 entries, 2020-06-15 to 2015-06-18\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Open    1257 non-null   float64\n",
      " 1   High    1257 non-null   float64\n",
      " 2   Low     1257 non-null   float64\n",
      " 3   Close   1257 non-null   float64\n",
      " 4   Volume  1253 non-null   float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 58.9 KB\n"
     ]
    }
   ],
   "source": [
    "sp500_stooq = web.DataReader('^SPX', 'stooq')\n",
    "sp500_stooq.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:26.786322Z",
     "start_time": "2020-06-16T16:31:26.763785Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-06-15</th>\n",
       "      <td>2993.76</td>\n",
       "      <td>3079.76</td>\n",
       "      <td>2965.66</td>\n",
       "      <td>3066.59</td>\n",
       "      <td>3.201624e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-12</th>\n",
       "      <td>3071.04</td>\n",
       "      <td>3088.42</td>\n",
       "      <td>2984.47</td>\n",
       "      <td>3041.31</td>\n",
       "      <td>3.379015e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-11</th>\n",
       "      <td>3123.53</td>\n",
       "      <td>3123.53</td>\n",
       "      <td>2999.49</td>\n",
       "      <td>3002.10</td>\n",
       "      <td>4.119365e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-10</th>\n",
       "      <td>3213.42</td>\n",
       "      <td>3223.27</td>\n",
       "      <td>3181.49</td>\n",
       "      <td>3190.14</td>\n",
       "      <td>3.577280e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-09</th>\n",
       "      <td>3213.32</td>\n",
       "      <td>3222.71</td>\n",
       "      <td>3193.11</td>\n",
       "      <td>3207.18</td>\n",
       "      <td>3.172476e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close        Volume\n",
       "Date                                                        \n",
       "2020-06-15  2993.76  3079.76  2965.66  3066.59  3.201624e+09\n",
       "2020-06-12  3071.04  3088.42  2984.47  3041.31  3.379015e+09\n",
       "2020-06-11  3123.53  3123.53  2999.49  3002.10  4.119365e+09\n",
       "2020-06-10  3213.42  3223.27  3181.49  3190.14  3.577280e+09\n",
       "2020-06-09  3213.32  3222.71  3193.11  3207.18  3.172476e+09"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500_stooq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:27.366773Z",
     "start_time": "2020-06-16T16:31:26.791344Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp500_stooq.Close.plot(figsize=(14,4))\n",
    "sns.despine()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NASDAQ Symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:27.604494Z",
     "start_time": "2020-06-16T16:31:27.372483Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 8875 entries, A to ZYXI\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype   \n",
      "---  ------            --------------  -----   \n",
      " 0   Nasdaq Traded     8875 non-null   bool    \n",
      " 1   Security Name     8875 non-null   object  \n",
      " 2   Listing Exchange  8875 non-null   category\n",
      " 3   Market Category   8875 non-null   object  \n",
      " 4   ETF               8875 non-null   bool    \n",
      " 5   Round Lot Size    8875 non-null   float64 \n",
      " 6   Test Issue        8875 non-null   bool    \n",
      " 7   Financial Status  3558 non-null   category\n",
      " 8   CQS Symbol        5317 non-null   object  \n",
      " 9   NASDAQ Symbol     8875 non-null   object  \n",
      " 10  NextShares        8875 non-null   bool    \n",
      "dtypes: bool(4), category(2), float64(1), object(4)\n",
      "memory usage: 468.4+ KB\n"
     ]
    }
   ],
   "source": [
    "from pandas_datareader.nasdaq_trader import get_nasdaq_symbols\n",
    "symbols = get_nasdaq_symbols()\n",
    "symbols.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tiingo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Requires [signing up](https://api.tiingo.com/) and storing API key in environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:27.807212Z",
     "start_time": "2020-06-16T16:31:27.609921Z"
    }
   },
   "outputs": [],
   "source": [
    "df = web.get_data_tiingo('GOOG', api_key=os.getenv('TIINGO_API_KEY'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T16:31:27.834076Z",
     "start_time": "2020-06-16T16:31:27.812635Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 1257 entries, ('GOOG', Timestamp('2015-06-18 00:00:00+0000', tz='UTC')) to ('GOOG', Timestamp('2020-06-15 00:00:00+0000', tz='UTC'))\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   close        1257 non-null   float64\n",
      " 1   high         1257 non-null   float64\n",
      " 2   low          1257 non-null   float64\n",
      " 3   open         1257 non-null   float64\n",
      " 4   volume       1257 non-null   int64  \n",
      " 5   adjClose     1257 non-null   float64\n",
      " 6   adjHigh      1257 non-null   float64\n",
      " 7   adjLow       1257 non-null   float64\n",
      " 8   adjOpen      1257 non-null   float64\n",
      " 9   adjVolume    1257 non-null   int64  \n",
      " 10  divCash      1257 non-null   float64\n",
      " 11  splitFactor  1257 non-null   float64\n",
      "dtypes: float64(10), int64(2)\n",
      "memory usage: 131.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "version": "3.7.7"
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   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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